In just about every field of work, there are quality measures in place to ensure customer satisfaction and product/service effectiveness. Manufacturing companies rely on quality control processes to minimize defects and reworks. Consultants measure the quality of their services to ensure repeat business. Journalist rely on quality information and leads to maintain integrity and credibility. But when it comes to corporate data, many organizations fail to understand the significance and drawbacks of unreliable or inconsistent data. This article discusses five quality challenges many organizations face and ways they can be more proactive in managing data.
Among the primary reasons for inconsistent or unusable data are:
* bad data from human data-entry error
* poorly-structured process
* lack of data standards across functional units or divisions
Ensuring the quality of data can become extremely difficult when you attempt to integrate data from across multiple sources. Before your organization begins a data-driven initiative it is important that you address issues of data quality within your existing data sources. Aside from the complexity of the actual process of ensuring the quality of your data, below are five challenges you may face when beginning this initiative:
* Data ownership
* Non standard data requirements
* Choosing the Right Data Management Tools
* Placing Responsibility for the Quality of Data on the IT Department
* Reactive vs. Proactive Mentality
Data Ownership
Data ownership, especially on the enterprise level, is a very complicated transition, and can contribute to significant pushback within an organization. Often the business unit managers or technicians entrusted with the implementation of an application assume ownership of the information used within that system. This introduces potential conflicts when these individuals must participate in enterprise-wide data initiatives and expose the internals of their information management to data quality audits and reviews.
Non Standard Data Requirements
Traditionally data management is structured where the business unit’s management chain has authority over the information used within the business unit, and each business unit has its own requirements for quality of data. Once data management progresses toward an enterprise-wide set of standards, there is often push back or hesitation by the business unit managers to invest time and resources in addressing issues that were not relevant at the business unit level.
Choosing the Right Data Management Tools
A frequent response by organizations with respect to building a data quality management program is to immediately begin to research the purchase of automated data cleansing or profiling tools. While some data quality tools do provide some benefit right out of the box, without a well-defined understanding of the types and scope of specific quality problems, and without a management plan for addressing discovered problems, buying a tool will not have a significant return on investment in achieving long-term strategic goals.
Placing Responsibility for the Quality of Data on the IT Department
Business units often assume that any issues regarding the quality of data are IT issues, and should be addressed by the technical teams. However, the business rules associated with running the business is best managed by the business client?
Reactive vs. Proactive Mentality
Most data quality programs are designed to react to data quality events instead of determining how to prevent problems from occurring in the first place. A mature data quality program determines where the risks are, what the objective metrics are for determining levels and impact of data quality compliance, and approaches to ensure high levels of quality.
Ways your organization can be more proactive towards data quality management:
* Ask for data quality performance measures as part of your business requirements gathering and prioritizing process.
* Determine, along with the business, how you are going to handle data quality issues both during the development process and when your processes are operational.
* Monitor data quality at every stage where data is touched
* Create a data quality management dashboard to monitor the agreed upon data quality performance measures.